def extract(html, url, **kwargs):
""" """
logging.debug("*** extracting %s ***" % (url,))
kw = { 'remove_comments': True }
if 'encoding' in kwargs:
kw['encoding'] = kwargs['encoding']
try:
foo = html.decode(kw['encoding'])
except UnicodeDecodeError:
# make it legal
logging.warning("Invalid %s - cleaning up" %(kw['encoding'],))
foo = html.decode(kw['encoding'],'ignore')
html = foo.encode(kw['encoding'])
parser = lxml.html.HTMLParser(**kw)
doc = lxml.html.document_fromstring(html, parser, base_url=url)
[i.drop_tree() for i in util.tags(doc,'script','style')]
# drop comment divs - they have a nasty habit of screwing things up
[i.drop_tree() for i in doc.cssselect('#disqus_thread')]
[i.drop_tree() for i in doc.cssselect('#comments, .comment')]
# drop obvious structural cruft
[i.drop_tree() for i in doc.cssselect('#header, #footer, #sidebar')]
# NASTY SITE-SPECIFIC HACKS
# nasty little hacks with no obvious general solutions:
# Johnston Publishing sites - they have adverts embedded in the headline :-(
[i.drop_tree() for i in doc.cssselect('.sponsorPanel')]
# www.shropshirestar.com
# www.expressandstar.com
# Have annoyingly-well marked up author links to featured articles in masthead
[i.drop_tree() for i in doc.cssselect('#masthead-quote')]
if 'independent.co.uk' in url:
[i.drop_tree() for i in doc.cssselect('#side, .readNextWidget')]
# html = UnicodeDammit(html, isHTML=True).markup
headline_info = extract_headline(doc,url)
headline_linenum = 0
headline = None
headline_node = None
if headline_info is not None:
headline_linenum = headline_info['sourceline']
headline = headline_info['txt']
headline_node = headline_info['node']
pubdate, pubdate_node = extract_pubdate(doc,url,headline_linenum)
authors = byline.extract(doc, url, headline_node, pubdate_node)
return headline,authors,pubdate
def extract(html, url, **kwargs):
""" """
logging.debug("*** extracting %s ***" % (url, ))
kw = {'remove_comments': True}
if 'encoding' in kwargs:
kw['encoding'] = kwargs['encoding']
try:
foo = html.decode(kw['encoding'])
except UnicodeDecodeError:
# make it legal
logging.warning("Invalid %s - cleaning up" % (kw['encoding'], ))
foo = html.decode(kw['encoding'], 'ignore')
html = foo.encode(kw['encoding'])
parser = lxml.html.HTMLParser(**kw)
doc = lxml.html.document_fromstring(html, parser, base_url=url)
[i.drop_tree() for i in util.tags(doc, 'script', 'style')]
# drop comment divs - they have a nasty habit of screwing things up
[i.drop_tree() for i in doc.cssselect('#disqus_thread')]
[i.drop_tree() for i in doc.cssselect('#comments, .comment')]
# drop obvious structural cruft
[i.drop_tree() for i in doc.cssselect('#header, #footer, #sidebar')]
# NASTY SITE-SPECIFIC HACKS
# nasty little hacks with no obvious general solutions:
# Johnston Publishing sites - they have adverts embedded in the headline :-(
[i.drop_tree() for i in doc.cssselect('.sponsorPanel')]
# www.shropshirestar.com
# www.expressandstar.com
# Have annoyingly-well marked up author links to featured articles in masthead
[i.drop_tree() for i in doc.cssselect('#masthead-quote')]
if 'independent.co.uk' in url:
[i.drop_tree() for i in doc.cssselect('#side, .readNextWidget')]
# html = UnicodeDammit(html, isHTML=True).markup
headline_info = extract_headline(doc, url)
headline_linenum = 0
headline = None
headline_node = None
if headline_info is not None:
headline_linenum = headline_info['sourceline']
headline = headline_info['txt']
headline_node = headline_info['node']
pubdate, pubdate_node = extract_pubdate(doc, url, headline_linenum)
authors = byline.extract(doc, url, headline_node, pubdate_node)
return headline, authors, pubdate
def pre_process_verbs(self, tagger="PerceptronTagger"):
"""This method extracts all verbs from each document in the documents collection
Keyword:
tagger -- tagger choosed. It could be among the following:
PerceptronTagger (default) and PatternTagger
"""
pt = util.get_tagger(tagger)
for ndoc in self.documents.find():
blob = pt(ndoc['text'])
verbs = []
for word in util.tags(blob):
is_verb = len(Word(word['raw']).get_synsets(pos=VERB)) > 0
if word['tag'] in util.PENN_VERBS_TAGS and is_verb:
verbs.append(word)
self.documents.update(
{'name': ndoc['name']}, {'$set': {'verbs': verbs}})
def extract(doc, url, headline_node, pubdate_node):
""" Returns byline text """
logging.debug("EXTRACTING BYLINE")
all = list(doc.iter())
candidates = {}
bylineContainer = doc
# TODO: REMOVE UGLY UGLY HACKERY!
if 'independent.co.uk/voices' in url:
foo = doc.cssselect('#top')
if len(foo) == 1:
bylineContainer = foo[0]
# TODO: early-out for special cases (eg hAtom author, rel="author")
for el in util.tags(bylineContainer, 'a', 'p', 'span', 'div', 'li', 'h3',
'h4', 'h5', 'h6', 'td', 'strong'):
authors, score = parse_byline(el, all, headline_node)
if score > 1.5:
# could be a date in it still
#txt = util.strip_date(txt).strip()
# reconstiute
txt = u" and ".join([a['name'] for a in authors])
candidates[el] = {'element': el, 'score': score, 'raw_byline': txt}
if candidates:
results = sorted(candidates.values(),
key=lambda item: item['score'],
reverse=True)
logging.debug(" byline rankings (top 10):")
for r in results[:10]:
logging.debug(" %.3f: '%s'" % (r['score'], r['raw_byline']))
return unicode(results[0]['raw_byline']).strip()
return None
def extract(doc, url, headline_node, pubdate_node):
""" Returns byline text """
logging.debug("EXTRACTING BYLINE")
all = list(doc.iter())
candidates = {}
bylineContainer=doc
# TODO: REMOVE UGLY UGLY HACKERY!
if 'independent.co.uk/voices' in url:
foo = doc.cssselect('#top')
if len(foo)==1:
bylineContainer = foo[0]
# TODO: early-out for special cases (eg hAtom author, rel="author")
for el in util.tags(bylineContainer, 'a','p','span','div','li','h3','h4','h5','h6','td','strong'):
authors, score = parse_byline(el, all, headline_node)
if score>1.5:
# could be a date in it still
#txt = util.strip_date(txt).strip()
# reconstiute
txt = u" and ".join([a['name'] for a in authors])
candidates[el] = {'element':el, 'score': score, 'raw_byline': txt}
if candidates:
results = sorted(candidates.values(), key=lambda item: item['score'], reverse=True)
logging.debug( " byline rankings (top 10):")
for r in results[:10]:
logging.debug(" %.3f: '%s'" % (r['score'], r['raw_byline']))
return unicode(results[0]['raw_byline']).strip()
return None
def extract_headline(doc,url):
logging.debug("extracting headline")
candidates = {}
for h in util.tags(doc,'h1','h2','h3','h4','h5','h6','div','span','td','th'):
score = 1
txt = unicode(h.text_content()).strip()
txt = u' '.join(txt.split())
if len(txt)==0:
continue
txt_norm = util.normalise_text(txt)
if len(txt)>=500:
continue
logging.debug(" headline: consider %s '%s'" % (h.tag,txt,))
# TODO: should run all these tests over a real corpus of articles
# and work out proper probability-based scoring!
# TEST: length of headline
# TODO - get a proper headline-length frequency curve from
# journalisted and score according to probability
if len(txt)>=20 and len(txt)<60:
logging.debug(" len in [20,60)")
score +=1
elif len(txt)>=25 and len(txt)<40:
logging.debug(" len in [25,40)")
score += 2
if h.tag in ('h1','h2','h3','h4'):
logging.debug(" significant heading (%s)" % (h.tag,))
score += 2
if h.tag in ('span','td'):
logging.debug(" -2 less headline-y element (%s)" % (h.tag,))
score -= 2
# TEST: does it appear in <title> text?
title = unicode(getattr(doc.find('.//title'), 'text', ''))
if title is not None:
if txt_norm in util.normalise_text(title):
logging.debug(" appears in <title>")
score += 3
# TEST: likely-looking class or id
if pats.headline['classes'].search(h.get('class','')):
logging.debug(" likely class")
score += 2
if pats.headline['classes'].search(h.get('id','')):
logging.debug(" likely id")
score += 2
# TEST: does it appear in likely looking <meta> tags?
# eg:
# <meta property="og:title" content="Dementia checks at age 75 urged"/>
# <meta name="Headline" content="Dementia checks at age 75 urged"/>
for meta in doc.findall('.//meta'):
n = meta.get('name', meta.get('property', ''))
if pats.headline['metatags'].search(n):
meta_content = util.normalise_text(unicode(meta.get('content','')))
if meta_content != '':
if txt_norm==meta_content:
logging.debug(" match meta")
score += 3
elif txt_norm in meta_content:
logging.debug(" contained by meta")
score += 1
# TEST: does it match slug part of url?
slug = re.split('[-_]', util.get_slug(url).lower())
parts = [util.normalise_text(part) for part in txt.split()]
parts = [part for part in parts if part!='']
if len(parts) > 1:
matched = [part for part in parts if part in slug]
value = (5.0*len(matched) / len(parts)) # max 5 points
if value > 0:
logging.debug(" match slug (%01f)" % (value,))
score += value
# TODO: other possible tests
# TEST: is it near top of article container?
# TEST: is it just above article container?
# TEST: is it non-complex html (anything more complex than <a>)
# TEST: is it outside likely sidebar elements?
if txt not in candidates or score > candidates[txt]:
candidates[txt] = {'txt':txt, 'score':score, 'sourceline':h.sourceline, 'node':h}
if not candidates:
return None
# sort
out = sorted(candidates.items(), key=lambda item: item[1]['score'], reverse=True)
#pprint(out[:5])
return out[0][1]
def extract_pubdate(doc, url, headline_linenum):
""" returns date,linenum """
candidates = {}
logging.debug("extracting pubdate")
# TODO: try some definitive meta tags first?
# "DCSext.articleFirstPublished"
# "DC.date.issued"
# "last-modified"
# check for date in slug
for pat in pats.pubdate['url_datefmts']:
m = pat.search(url)
if m is not None:
d = datetime.datetime( int(m.group('year')), int(m.group('month')), int(m.group('day')) )
logging.debug(" using %s from url" % (d,))
return d,None
meta_dates = set()
for meta in doc.findall('.//meta'):
n = meta.get('name', meta.get('property', ''))
if pats.pubdate['metatags'].search(n):
logging.debug(" date: consider meta name='%s' content='%s'" % (n,meta.get('content','')))
fuzzy = fuzzydate.parse_datetime(meta.get('content',''))
if not fuzzy.empty_date():
meta_dates.add(fuzzy.date(fuzzydate.fuzzydate(day=1)))
# if len(meta_dates)==1:
# # only one likely-looking <meta> entry - lets go with it
# d = list(meta_dates)[0]
# logging.debug(" using %s from <meta>" % (d,))
# return d,None
# start looking through whole page
for e in util.tags(doc,'p','span','div','li','td','th','h4','h5','h6','font'):
txt = unicode(e.text_content()).strip()
txt = u' '.join(txt.split())
# discard anything too short or long
if len(txt)<6 or len(txt) > 150:
continue
score = 1
dt = extract_date(txt)
if dt is None:
continue
logging.debug(" date: considering %s '%s'" % (e.tag,txt))
# TEST: proximity to headline in html
if headline_linenum>0 and e.sourceline>0:
dist = e.sourceline - headline_linenum
if dist >-10 and dist <25:
logging.debug(" near headline")
score += 1
# TEST: likely class or id?
if pats.pubdate['classes'].search(e.get('class','')):
logging.debug(" likely class")
score += 1
if pats.pubdate['classes'].search(e.get('id','')):
logging.debug(" likely id")
score += 1
# in byline is also a good indicator
if pats.byline['classes'].search(e.get('class','')):
logging.debug(" likely class")
score += 1
if pats.byline['classes'].search(e.get('id','')):
logging.debug(" likely id")
score += 1
# TEST: also appears in likely <meta> tags?
if dt.date() in meta_dates:
logging.debug(" appears in <meta>")
score += 1
# TEST: not within likely-looking comment container?
in_comment = False
foo = e.getparent()
while foo is not None:
if pats.pubdate['comment_classes'].search(foo.get('class','')):
in_comment = True
break
foo = foo.getparent()
if not in_comment:
logging.debug(" not inside likely comment")
score += 1
# TEST: indicative text? ("posted on" , "last updated" etc...)
if pats.pubdate['pubdate_indicator'].search(txt):
logging.debug(" text indicative of pubdate")
score += 1
# TEST: date appears in url? eg "http://blah.com/blahblah-20100801-blah.html"
if re.compile("%d[-_/.]?0?%d[-_/.]?0?%d" % (dt.year,dt.month,dt.day)).search(url):
logging.debug(" full date appears in url")
score += 2
elif re.compile("%d[-_/.]?0?%d" % (dt.year,dt.month)).search(url):
logging.debug(" year and month appear in url")
score += 1
if dt.date() not in candidates or score>candidates[dt.date()]['score']:
candidates[dt.date()] = {'datetime': dt, 'score': score, 'node': e}
if not candidates:
return None,None
out = sorted(candidates.items(), key=lambda item: item[1]['score'], reverse=True)
# print "========="
# pprint( out[:5] )
# print "========="
best = out[0][1]
return best['datetime'],best['node']
def extract_headline(doc, url):
logging.debug("extracting headline")
candidates = {}
for h in util.tags(doc, 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'div', 'span',
'td', 'th'):
score = 1
txt = unicode(h.text_content()).strip()
txt = u' '.join(txt.split())
if len(txt) == 0:
continue
txt_norm = util.normalise_text(txt)
if len(txt) >= 500:
continue
logging.debug(" headline: consider %s '%s'" % (
h.tag,
txt,
))
# TODO: should run all these tests over a real corpus of articles
# and work out proper probability-based scoring!
# TEST: length of headline
# TODO - get a proper headline-length frequency curve from
# journalisted and score according to probability
if len(txt) >= 20 and len(txt) < 60:
logging.debug(" len in [20,60)")
score += 1
elif len(txt) >= 25 and len(txt) < 40:
logging.debug(" len in [25,40)")
score += 2
if h.tag in ('h1', 'h2', 'h3', 'h4'):
logging.debug(" significant heading (%s)" % (h.tag, ))
score += 2
if h.tag in ('span', 'td'):
logging.debug(" -2 less headline-y element (%s)" % (h.tag, ))
score -= 2
# TEST: does it appear in <title> text?
title = unicode(getattr(doc.find('.//title'), 'text', ''))
if title is not None:
if txt_norm in util.normalise_text(title):
logging.debug(" appears in <title>")
score += 3
# TEST: likely-looking class or id
if pats.headline['classes'].search(h.get('class', '')):
logging.debug(" likely class")
score += 2
if pats.headline['classes'].search(h.get('id', '')):
logging.debug(" likely id")
score += 2
# TEST: does it appear in likely looking <meta> tags?
# eg:
# <meta property="og:title" content="Dementia checks at age 75 urged"/>
# <meta name="Headline" content="Dementia checks at age 75 urged"/>
for meta in doc.findall('.//meta'):
n = meta.get('name', meta.get('property', ''))
if pats.headline['metatags'].search(n):
meta_content = util.normalise_text(
unicode(meta.get('content', '')))
if meta_content != '':
if txt_norm == meta_content:
logging.debug(" match meta")
score += 3
elif txt_norm in meta_content:
logging.debug(" contained by meta")
score += 1
# TEST: does it match slug part of url?
slug = re.split('[-_]', util.get_slug(url).lower())
parts = [util.normalise_text(part) for part in txt.split()]
parts = [part for part in parts if part != '']
if len(parts) > 1:
matched = [part for part in parts if part in slug]
value = (5.0 * len(matched) / len(parts)) # max 5 points
if value > 0:
logging.debug(" match slug (%01f)" % (value, ))
score += value
# TODO: other possible tests
# TEST: is it near top of article container?
# TEST: is it just above article container?
# TEST: is it non-complex html (anything more complex than <a>)
# TEST: is it outside likely sidebar elements?
if txt not in candidates or score > candidates[txt]:
candidates[txt] = {
'txt': txt,
'score': score,
'sourceline': h.sourceline,
'node': h
}
if not candidates:
return None
# sort
out = sorted(candidates.items(),
key=lambda item: item[1]['score'],
reverse=True)
#pprint(out[:5])
return out[0][1]
def extract_pubdate(doc, url, headline_linenum):
""" returns date,linenum """
candidates = {}
logging.debug("extracting pubdate")
# TODO: try some definitive meta tags first?
# "DCSext.articleFirstPublished"
# "DC.date.issued"
# "last-modified"
# check for date in slug
for pat in pats.pubdate['url_datefmts']:
m = pat.search(url)
if m is not None:
d = datetime.datetime(int(m.group('year')), int(m.group('month')),
int(m.group('day')))
logging.debug(" using %s from url" % (d, ))
return d, None
meta_dates = set()
for meta in doc.findall('.//meta'):
n = meta.get('name', meta.get('property', ''))
if pats.pubdate['metatags'].search(n):
logging.debug(" date: consider meta name='%s' content='%s'" %
(n, meta.get('content', '')))
fuzzy = fuzzydate.parse_datetime(meta.get('content', ''))
if not fuzzy.empty_date():
meta_dates.add(fuzzy.date(fuzzydate.fuzzydate(day=1)))
# if len(meta_dates)==1:
# # only one likely-looking <meta> entry - lets go with it
# d = list(meta_dates)[0]
# logging.debug(" using %s from <meta>" % (d,))
# return d,None
# start looking through whole page
for e in util.tags(doc, 'p', 'span', 'div', 'li', 'td', 'th', 'h4', 'h5',
'h6', 'font'):
txt = unicode(e.text_content()).strip()
txt = u' '.join(txt.split())
# discard anything too short or long
if len(txt) < 6 or len(txt) > 150:
continue
score = 1
dt = extract_date(txt)
if dt is None:
continue
logging.debug(" date: considering %s '%s'" % (e.tag, txt))
# TEST: proximity to headline in html
if headline_linenum > 0 and e.sourceline > 0:
dist = e.sourceline - headline_linenum
if dist > -10 and dist < 25:
logging.debug(" near headline")
score += 1
# TEST: likely class or id?
if pats.pubdate['classes'].search(e.get('class', '')):
logging.debug(" likely class")
score += 1
if pats.pubdate['classes'].search(e.get('id', '')):
logging.debug(" likely id")
score += 1
# in byline is also a good indicator
if pats.byline['classes'].search(e.get('class', '')):
logging.debug(" likely class")
score += 1
if pats.byline['classes'].search(e.get('id', '')):
logging.debug(" likely id")
score += 1
# TEST: also appears in likely <meta> tags?
if dt.date() in meta_dates:
logging.debug(" appears in <meta>")
score += 1
# TEST: not within likely-looking comment container?
in_comment = False
foo = e.getparent()
while foo is not None:
if pats.pubdate['comment_classes'].search(foo.get('class', '')):
in_comment = True
break
foo = foo.getparent()
if not in_comment:
logging.debug(" not inside likely comment")
score += 1
# TEST: indicative text? ("posted on" , "last updated" etc...)
if pats.pubdate['pubdate_indicator'].search(txt):
logging.debug(" text indicative of pubdate")
score += 1
# TEST: date appears in url? eg "http://blah.com/blahblah-20100801-blah.html"
if re.compile("%d[-_/.]?0?%d[-_/.]?0?%d" %
(dt.year, dt.month, dt.day)).search(url):
logging.debug(" full date appears in url")
score += 2
elif re.compile("%d[-_/.]?0?%d" % (dt.year, dt.month)).search(url):
logging.debug(" year and month appear in url")
score += 1
if dt.date(
) not in candidates or score > candidates[dt.date()]['score']:
candidates[dt.date()] = {'datetime': dt, 'score': score, 'node': e}
if not candidates:
return None, None
out = sorted(candidates.items(),
key=lambda item: item[1]['score'],
reverse=True)
# print "========="
# pprint( out[:5] )
# print "========="
best = out[0][1]
return best['datetime'], best['node']
